Extrapolation of information collected at fine scales to broader scales is an increasingly important issue in ecology as the recognition of spatial connections within and among different levels of organization expands. In addition, our ability to represent complex behavior in ecological systems has improved with readily available instrumentation and software that allows detailed sampling and analysis and the ease with which geospatial data can be used to support large spatial simulation models or extensive data-based inventories and assessments. However, there is a tradeoff between simple approaches having errors associated with excluding processes and more complex approaches that are plagued by high uncertainty due to increased estimation and measurement error. In this paper, we develop a synthetic, problem-solving framework for extrapolating information from fine to broad scales that is applicable to a wide range of ecological problems. The framework includes three classes of approaches that differ in their complexity and sources of error: nonspatial, spatially implicit, and spatially explicit. We consider a range of ecological issues requiring complex approaches (i.e., nonlinear processes, thresholds, positive feedbacks, neighborhood processes) and identify options for dealing with these issues. Our operational framework of model selection serves as a practical and objective approach to the problem of ecological prediction across a range of spatial and temporal scales. Thus, this framework should be of interest to all ecologists concerned with the problem of prediction.